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Sequence-Based Prediction of Protein Secretion Success in Aspergillus niger Read chapter Read first chapter

2010 | OriginalPaper | Chapter

Iterated Local Search for Biclustering of Microarray Data

Authors : Wassim Ayadi, Mourad Elloumi, Jin-Kao Hao

Published in: Pattern Recognition in Bioinformatics

Publisher: Springer Berlin Heidelberg

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In the context of microarray data analysis, biclustering aims to identify simultaneously a group of genes that are highly correlated across a group of experimental conditions. This paper presents a Biclustering Iterative Local Search (BILS) algorithm to the problem of biclustering of microarray data. The proposed algorithm is highlighted by the use of some original features including a new evaluation function, a dedicated neighborhood relation and a tailored perturbation strategy. The BILS algorithm is assessed on the well-known yeast cell-cycle dataset and compared with two most popular algorithms.

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previous chapter Semi-Supervised Graph Embedding Scheme with Active Learning (SSGEAL): Classifying High Dimensional Biomedical Data
next chapter Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of Expression Microarray
Metadata
Title
Iterated Local Search for Biclustering of Microarray Data
Authors
Wassim Ayadi
Mourad Elloumi
Jin-Kao Hao
Copyright Year
2010
Publisher
Springer Berlin Heidelberg
Book
Pattern Recognition in Bioinformatics

Print ISBN: 978-3-642-16000-4

Electronic ISBN: 978-3-642-16001-1

Copyright Year: 2010

DOI
https://doi.org/10.1007/978-3-642-16001-1_19